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Abstract
Atmospheric science is male dominated and few students of color matriculate into the field, a trend dating back at least 50 years. UCAR/NCAR Equity and Inclusion (referred to as UNEION), which has trained nearly 200 employees, is the institution’s flagship diversity program. UNEION is central to efforts to create a welcoming workplace, engaging participants with peer-led learning to gain knowledge on diversity, equity, and inclusion (DEI) topics, and encouraging participants to implement these learnings through bystander intervention. Evaluation results show that UNEION 1) increases participants’ awareness of inequities, 2) encourages participants to feel responsible for DEI, and 3) teaches participants how to intervene in inappropriate situations.
Abstract
Atmospheric science is male dominated and few students of color matriculate into the field, a trend dating back at least 50 years. UCAR/NCAR Equity and Inclusion (referred to as UNEION), which has trained nearly 200 employees, is the institution’s flagship diversity program. UNEION is central to efforts to create a welcoming workplace, engaging participants with peer-led learning to gain knowledge on diversity, equity, and inclusion (DEI) topics, and encouraging participants to implement these learnings through bystander intervention. Evaluation results show that UNEION 1) increases participants’ awareness of inequities, 2) encourages participants to feel responsible for DEI, and 3) teaches participants how to intervene in inappropriate situations.
Abstract
Adapted from the sports concept of scorigami, the weathergami chart is introduced. Weathergami charts depict the frequency of occurrence of the full range of daily maximum and minimum temperature combinations observed at a location. These charts highlight essential features of climate not evident in traditional representations. A variation of the weathergami chart displays transition frequencies, which describe the likelihood of particular day-to-day changes in maximum and minimum temperatures. Likewise, weathergami anomaly charts reveal characteristics of changing climate not evident in standard time series representations. Several examples are provided, with comparisons to climate descriptions found in popular textbooks.
Abstract
Adapted from the sports concept of scorigami, the weathergami chart is introduced. Weathergami charts depict the frequency of occurrence of the full range of daily maximum and minimum temperature combinations observed at a location. These charts highlight essential features of climate not evident in traditional representations. A variation of the weathergami chart displays transition frequencies, which describe the likelihood of particular day-to-day changes in maximum and minimum temperatures. Likewise, weathergami anomaly charts reveal characteristics of changing climate not evident in standard time series representations. Several examples are provided, with comparisons to climate descriptions found in popular textbooks.
Abstract
Verif is an open-source tool for verifying weather predictions against a ground truth. It is suitable for a range of applications and designed for iterative product development involving fine-tuning of algorithms, comparing methods, and addressing scientific issues with the product. The tool generates verification plots based on user-supplied input files containing predictions and observations for multiple point-locations, forecast lead times, and forecast initialization times. It supports over 90 verification metrics and diagrams and can evaluate deterministic and probabilistic predictions. An extensive set of command-line flags control how the input data are aggregated, filtered, stratified, and visualized. The broad range of metrics and data manipulation options allows the user to gain insight from both summary scores and detailed time series of individual weather events. Verif is suitable for many applications, including assessing numerical weather prediction models, climate models, reanalyses, machine learning models, and even the fidelity of emerging observational sources. The tool has matured through long-term development at the Norwegian Meteorological Institute and the University of British Columbia. Verif comes with an extensive wiki page and example input files covering a wide range of prediction applications, allowing students and researchers interested in verification to get hands-on experience with real-life datasets. This article describes the functionality of Verif version 1.3 and shows how the tool can be used for effective product development.
Abstract
Verif is an open-source tool for verifying weather predictions against a ground truth. It is suitable for a range of applications and designed for iterative product development involving fine-tuning of algorithms, comparing methods, and addressing scientific issues with the product. The tool generates verification plots based on user-supplied input files containing predictions and observations for multiple point-locations, forecast lead times, and forecast initialization times. It supports over 90 verification metrics and diagrams and can evaluate deterministic and probabilistic predictions. An extensive set of command-line flags control how the input data are aggregated, filtered, stratified, and visualized. The broad range of metrics and data manipulation options allows the user to gain insight from both summary scores and detailed time series of individual weather events. Verif is suitable for many applications, including assessing numerical weather prediction models, climate models, reanalyses, machine learning models, and even the fidelity of emerging observational sources. The tool has matured through long-term development at the Norwegian Meteorological Institute and the University of British Columbia. Verif comes with an extensive wiki page and example input files covering a wide range of prediction applications, allowing students and researchers interested in verification to get hands-on experience with real-life datasets. This article describes the functionality of Verif version 1.3 and shows how the tool can be used for effective product development.
Abstract
It is common when speaking colloquially to describe climate as the average weather, which implies weather is the driver and climatic averages are a passive by-product of it, but it is useful to reframe this toward weather being the “expression” of climate. That is, a region’s climate defines the range of weather it might experience (including the extent and frequency of extremes). In this framing, weather is driven by a region’s climate. A changing climate then, necessarily, is experienced as a change in local weather events—often most visibly through changes in the extent or frequency of extreme weather.
Abstract
It is common when speaking colloquially to describe climate as the average weather, which implies weather is the driver and climatic averages are a passive by-product of it, but it is useful to reframe this toward weather being the “expression” of climate. That is, a region’s climate defines the range of weather it might experience (including the extent and frequency of extremes). In this framing, weather is driven by a region’s climate. A changing climate then, necessarily, is experienced as a change in local weather events—often most visibly through changes in the extent or frequency of extreme weather.
Abstract
Many factors shape public perceptions of extreme weather risk; understanding these factors is important to encourage preparedness. This article describes a novel workshop designed to encourage individual and community decision-making about predicted storm surge flooding. Over 160 U.S. college students participated in this 4-h experience. Distinctive features included 1) two kinds of visualizations, standard weather forecasting graphics versus 3D computer graphics visualization; 2) narrative about a fictitious storm, role-play, and guided discussion of participants’ concerns; and 3) use of an “ethical matrix,” a collective decision-making tool that elicits diverse perspectives based on the lived experiences of diverse stakeholders. Participants experienced a narrative about a hurricane with potential for devastating storm surge flooding on a fictitious coastal college campus. They answered survey questions before, at key points during, and after the narrative, interspersed with forecasts leading to predicted storm landfall. During facilitated breakout groups, participants role-played characters and filled out an ethical matrix. Discussing the matrix encouraged consideration of circumstances impacting evacuation decisions. Participants’ comments suggest several components may have influenced perceptions of personal risk, risks to others, the importance of monitoring weather, and preparing for emergencies. Surprisingly, no differences between the standard forecast graphics versus the immersive, hyperlocal visualizations were detected. Overall, participants’ comments indicate the workshop increased appreciation of others’ evacuation and preparation challenges.
Abstract
Many factors shape public perceptions of extreme weather risk; understanding these factors is important to encourage preparedness. This article describes a novel workshop designed to encourage individual and community decision-making about predicted storm surge flooding. Over 160 U.S. college students participated in this 4-h experience. Distinctive features included 1) two kinds of visualizations, standard weather forecasting graphics versus 3D computer graphics visualization; 2) narrative about a fictitious storm, role-play, and guided discussion of participants’ concerns; and 3) use of an “ethical matrix,” a collective decision-making tool that elicits diverse perspectives based on the lived experiences of diverse stakeholders. Participants experienced a narrative about a hurricane with potential for devastating storm surge flooding on a fictitious coastal college campus. They answered survey questions before, at key points during, and after the narrative, interspersed with forecasts leading to predicted storm landfall. During facilitated breakout groups, participants role-played characters and filled out an ethical matrix. Discussing the matrix encouraged consideration of circumstances impacting evacuation decisions. Participants’ comments suggest several components may have influenced perceptions of personal risk, risks to others, the importance of monitoring weather, and preparing for emergencies. Surprisingly, no differences between the standard forecast graphics versus the immersive, hyperlocal visualizations were detected. Overall, participants’ comments indicate the workshop increased appreciation of others’ evacuation and preparation challenges.
Abstract
Many of our generation’s most pressing environmental science problems are wicked problems, which means they cannot be cleanly isolated and solved with a single “correct” answer. The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) seeks to address such problems by developing synergistic approaches with a team of scientists from three disciplines: environmental science (including atmospheric, ocean, and other physical sciences), artificial intelligence (AI), and social science including risk communication. As part of our work, we developed a novel approach to summer school, held from 27 to 30 June 2022. The goal of this summer school was to teach a new generation of environmental scientists how to cross disciplines and develop approaches that integrate all three disciplinary perspectives and approaches in order to solve environmental science problems. In addition to a lecture series that focused on the synthesis of AI, environmental science, and risk communication, this year’s summer school included a unique “trust-a-thon” component where participants gained hands-on experience applying both risk communication and explainable AI techniques to pretrained machine learning models. We had 677 participants from 63 countries register and attend online. Lecture topics included trust and trustworthiness (day 1), explainability and interpretability (day 2), data and workflows (day 3), and uncertainty quantification (day 4). For the trust-a-thon, we developed challenge problems for three different application domains: 1) severe storms, 2) tropical cyclones, and 3) space weather. Each domain had associated user persona to guide user-centered development.
Abstract
Many of our generation’s most pressing environmental science problems are wicked problems, which means they cannot be cleanly isolated and solved with a single “correct” answer. The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) seeks to address such problems by developing synergistic approaches with a team of scientists from three disciplines: environmental science (including atmospheric, ocean, and other physical sciences), artificial intelligence (AI), and social science including risk communication. As part of our work, we developed a novel approach to summer school, held from 27 to 30 June 2022. The goal of this summer school was to teach a new generation of environmental scientists how to cross disciplines and develop approaches that integrate all three disciplinary perspectives and approaches in order to solve environmental science problems. In addition to a lecture series that focused on the synthesis of AI, environmental science, and risk communication, this year’s summer school included a unique “trust-a-thon” component where participants gained hands-on experience applying both risk communication and explainable AI techniques to pretrained machine learning models. We had 677 participants from 63 countries register and attend online. Lecture topics included trust and trustworthiness (day 1), explainability and interpretability (day 2), data and workflows (day 3), and uncertainty quantification (day 4). For the trust-a-thon, we developed challenge problems for three different application domains: 1) severe storms, 2) tropical cyclones, and 3) space weather. Each domain had associated user persona to guide user-centered development.
Abstract
During the second half of the twentieth century, the West Antarctic Ice Sheet (WAIS) has undergone significant warming at more than twice the global mean and thus is regarded as one of the most rapidly warming regions on Earth. However, a reversal of this trend was observed in the 1990s, resulting in regional cooling. In particular, during 1999–2018, the observed annual average surface air temperature had decreased at a statistically significant rate, with the strongest cooling in austral spring. The spring cooling correlates significantly with the second leading modes (EOF2) derived from empirical orthogonal function (EOF) analysis on the sea level pressure over Antarctica during 1999–2018, associated with the negative phase of the interdecadal Pacific oscillation with an average of cooling of central and eastern tropical Pacific surface sea temperature (SST) anomalies. The EOF2 results in the enhanced cold southerly winds on the continental WAIS through the cyclonic conditions over the Amundsen Sea region and a blocking high in the Drake Passage and northern Antarctic Peninsula, causing the WAIS cooling trend.
Abstract
During the second half of the twentieth century, the West Antarctic Ice Sheet (WAIS) has undergone significant warming at more than twice the global mean and thus is regarded as one of the most rapidly warming regions on Earth. However, a reversal of this trend was observed in the 1990s, resulting in regional cooling. In particular, during 1999–2018, the observed annual average surface air temperature had decreased at a statistically significant rate, with the strongest cooling in austral spring. The spring cooling correlates significantly with the second leading modes (EOF2) derived from empirical orthogonal function (EOF) analysis on the sea level pressure over Antarctica during 1999–2018, associated with the negative phase of the interdecadal Pacific oscillation with an average of cooling of central and eastern tropical Pacific surface sea temperature (SST) anomalies. The EOF2 results in the enhanced cold southerly winds on the continental WAIS through the cyclonic conditions over the Amundsen Sea region and a blocking high in the Drake Passage and northern Antarctic Peninsula, causing the WAIS cooling trend.
Abstract
The 2018 exceptional drought over the Colorado Plateau motivated unprecedented responses by individuals and organizations. Some of these responses made clear that proactive adaptive measures were fundamental to drought resilience. Climate service organizations (CSOs) supporting and observing these responses realized the utility of a network to share and document successful drought responses. In February 2020, a small group of CSOs and resource managers (RMs) met to envision the Southwest Drought Learning Network (DLN) to align with other existing efforts, but with the specific goal of enabling peer-to-peer learning to build resilience to future droughts. Since then, the network has grown into five organized teams focused on specific aspects of building drought resilience. Team activities include sharing case studies to help others learn from past experiences, hosting monthly drought briefings that introduce drought data and management tools, identifying information needed to support critical management decisions, innovating and sharing new and traditional drought monitoring technologies, and building drought resilience with indigenous communities. The network allows for collaboration and leveraging partner resources and strengths. The DLN website (https://dln.swclimatehub.info/) hosts more information about network teams and activities. This innovative network continues to grow in response to management needs and water scarcity in the region. For the benefit of others who may be considering a similar network and supporting peer-to-peer learning, we document the history, process, and lessons learned regarding the Southwest DLN.
Abstract
The 2018 exceptional drought over the Colorado Plateau motivated unprecedented responses by individuals and organizations. Some of these responses made clear that proactive adaptive measures were fundamental to drought resilience. Climate service organizations (CSOs) supporting and observing these responses realized the utility of a network to share and document successful drought responses. In February 2020, a small group of CSOs and resource managers (RMs) met to envision the Southwest Drought Learning Network (DLN) to align with other existing efforts, but with the specific goal of enabling peer-to-peer learning to build resilience to future droughts. Since then, the network has grown into five organized teams focused on specific aspects of building drought resilience. Team activities include sharing case studies to help others learn from past experiences, hosting monthly drought briefings that introduce drought data and management tools, identifying information needed to support critical management decisions, innovating and sharing new and traditional drought monitoring technologies, and building drought resilience with indigenous communities. The network allows for collaboration and leveraging partner resources and strengths. The DLN website (https://dln.swclimatehub.info/) hosts more information about network teams and activities. This innovative network continues to grow in response to management needs and water scarcity in the region. For the benefit of others who may be considering a similar network and supporting peer-to-peer learning, we document the history, process, and lessons learned regarding the Southwest DLN.
Abstract
Further long-term investments in high-quality, research-driven, fit-for-purpose observations of atmospheric composition are needed globally to meet urgent societal needs related to weather, climate, air quality, and other environmental issues. Challenges include maintaining current observing systems in the face of eroding budgets for long-term monitoring and filling the geographical gaps for key constituents needed for sound services and policies. The observing systems can be bolstered through science-for-services applications, by embracing interoperable observation systems and standardized metadata, and ensuring that the data are findable, accessible, interoperable, and reusable. There is an urgent need to move from opportunities-driven one-component observations to more systematic, planned multifunctional infrastructure, where the observational data flow is coupled with Earth system models to serve both operational and research purposes. This approach fosters a community where user experience feeds back into the research components and where mature research results are translated into operational applications. This will lead to faster exploration and exploitation of atmospheric composition information and more impactful applications for science and society. We discuss here the urgent need to (i) achieve global coverage, (ii) harmonize infrastructure operations, (iii) establish focused policies, and (iv) strengthen coordination of atmospheric composition infrastructure.
Abstract
Further long-term investments in high-quality, research-driven, fit-for-purpose observations of atmospheric composition are needed globally to meet urgent societal needs related to weather, climate, air quality, and other environmental issues. Challenges include maintaining current observing systems in the face of eroding budgets for long-term monitoring and filling the geographical gaps for key constituents needed for sound services and policies. The observing systems can be bolstered through science-for-services applications, by embracing interoperable observation systems and standardized metadata, and ensuring that the data are findable, accessible, interoperable, and reusable. There is an urgent need to move from opportunities-driven one-component observations to more systematic, planned multifunctional infrastructure, where the observational data flow is coupled with Earth system models to serve both operational and research purposes. This approach fosters a community where user experience feeds back into the research components and where mature research results are translated into operational applications. This will lead to faster exploration and exploitation of atmospheric composition information and more impactful applications for science and society. We discuss here the urgent need to (i) achieve global coverage, (ii) harmonize infrastructure operations, (iii) establish focused policies, and (iv) strengthen coordination of atmospheric composition infrastructure.
Abstract
While numerous collaborations exist between the atmospheric sciences research community and the U.S. National Weather Service (NWS), collaborative research field studies between undergraduate (UG) students at universities and the NWS are less common. The Summertime Canyon Observations and Research to Characterize Heat Extreme Regimes (SCORCHER) study was an UG student-driven research field campaign conducted in Palo Duro Canyon State Park, Texas, United States, during the summer of 2021. The SCORCHER campaign was mainly aimed at improving our basic scientific understanding of extreme heat, public safety, and forecasting applications, and creating an empowering UG educational field research experience. This “In Box” article highlights the collaborative study design, execution, and lessons learned.
Abstract
While numerous collaborations exist between the atmospheric sciences research community and the U.S. National Weather Service (NWS), collaborative research field studies between undergraduate (UG) students at universities and the NWS are less common. The Summertime Canyon Observations and Research to Characterize Heat Extreme Regimes (SCORCHER) study was an UG student-driven research field campaign conducted in Palo Duro Canyon State Park, Texas, United States, during the summer of 2021. The SCORCHER campaign was mainly aimed at improving our basic scientific understanding of extreme heat, public safety, and forecasting applications, and creating an empowering UG educational field research experience. This “In Box” article highlights the collaborative study design, execution, and lessons learned.
